237 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" Postdoctoral positions at Nature Careers
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
machine learning methods are a plus. Qualifications: PhD in neuroscience, or related fields DeepLabCut or similar methods Demonstrated hands-on experience with 2-photon imaging techniques Experience
-
Engineering or a related field The ideal candidate should have some knowledge and experience in the following topics: Software Cybersecurity Software Testing and Analysis Machine Learning and Multimodal Large
-
of image processing e.g. using machine learning German skills For further questions, contact Dr. lmke Greving (imke.greving@hereon.de). We offer you an exciting and varied job in a research centre with
-
, biochemical, cell, and tissue biology method skills. Experience in using computational analysis (biostatistics, machine learning, data science, physics, or a related field). We value diversity and strongly
-
key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
-
, ATAC-seq, CUT&RUN, MERFISH, Visium), epigenomic data processing (chromatin accessibility, histone marks, enhancer mapping), multi-omics integration using Seurat, Signac, Harmony, ArchR or Scanpy, machine
-
biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
-
-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
-
individuals. iPSC “Village” systems and CRISPR perturbation to experimentally dissect and validate gene function in controlled, scalable cellular models. Advanced computational genomics, machine learning, and
-
, machine learning, and causal inference frameworks that link genetic variants to cellular mechanisms and therapeutic opportunities. Our research spans immune biology, cardiac disease, neurodegeneration, and